摘要： 课程推荐对于提升学习者学习效率和参与度至关重要，而学习者的学习序列建模是课程推荐的关键环节，因为这些序列不仅包含了学习者的动态学习兴趣，还隐含了学习行为的演化规律。然而，现有方法主要关注序列中的顺序关系，未能有效考虑课程之间的时间间隔对学习者兴趣动态演化的影响。此外，大多数模型仅用单一向量来表征学习者行为，无法刻画其多维度学习兴趣的动态演化过程以及不同兴趣之间的关联，导致兴趣建模存在偏差。针对上述问题，提出一种时间间隔增强的多兴趣动态演化网络课程推荐模型TIMIR。将学习者的交互历史视为具有不同时间间隔的序列，提出基于时空的双重自注意力机制，通过设计双路径机制区分长短期时间间隔的差异化影响，精准捕捉学习者兴趣动态演化的持续与转移模式；结合胶囊网络中的动态路由机制，自适应地生成多个学习者兴趣向量；构建多兴趣动态演化网络，显式建模学习者多兴趣的时序演变和多个兴趣之间的关联，从而提升长期学习行为的预测精度与复杂兴趣场景下的推荐覆盖率。在MOOCCourse数据集上的实验结果表明，TIMIR在HT@20和NDCG@20两个指标上分别优于其他高级推荐模型2.56%和4.18%；在MOOCCube数据集上，两个指标分别优于其他高级推荐模型1.27%和1.71%，验证了其在提升推荐准确性能方面的有效性。

Abstract: Course recommendation is crucial for enhancing learners' learning efficiency and engagement, and modeling learners' learning sequences is a key part of course recommendation, because these sequences not only contain learners' dynamic learning interests, but also imply the evolutionary law of learning behaviors. However, existing methods focus on sequential relationships in sequences and fail to consider the impact of the time interval between courses on the dynamic evolution of learners' interests. In addition, most models characterize learners' behaviors with a single vector, failing to portray the dynamic evolution process of their multidimensional learning interests and the associations between different interests, resulting in biased interest modeling. To address these issues, this paper proposes a time interval-enhanced multi-interest dynamic evolution network for course recommendation(TIMIR). The method treats learners’ interaction history as sequences with different time intervals and designs a spatio-temporal dual self-attention mechanism to capture the persistence and transfer patterns of learners' interest dynamic evolution by designing a dual-path mechanism to differentiate the differential effects of long and short-term time intervals; generating multiple learner interest vectors by combining with the dynamic routing mechanism in the capsule network; and constructing a multi-interest dynamic evolution network, modeling the temporal evolution of learners' multi-interests and the associations between multiple interests, so as to improve the prediction accuracy of long-term learning behaviors and recommendation coverage in complex interest scenarios. Experimental results on the MOOCCourse dataset demonstrate that TIMIR outperforms other advanced recommendation models by 2.56% on HT@20 and 4.18% on NDCG@20; on the MOOCCube dataset, the two metrics outperform other advanced recommendation models by 1.27% and 1.71%, respectively, validating its effectiveness in enhancing recommendation accuracy.